Web Science and Digital Libraries Research Group

                   Figure 1. An illustration of the differences between aleatoric and epistemic uncertainties (Yang et al., 2023). Introduction Table Structure Recognition (TSR) is a task of document analysis that focuses on identifying rows and columns in digital table images [4]. While current TSR methods can identify cell locations, they lack the ability to predict uncertainties in their results [1]. This limitation has hindered the real-world application of TSR, such as automatically extracting data from table images in physical sciences. In this blog post, we summarize our paper titled " Uncertainty Quantification (UQ) for Table Structure Recognition ", presented at the 2024 IEEE International Conference on Information Reuse and Integration for Data Science . In this paper, we proposed a method called TTA-m (Test-Time Augmentation with multiple models) that aims to quantify uncertainties in TSR predictions, potentially enhancing ho...

Introduction The realm of concerns surrounding reproducibility, replicability, and generalizability (RR&G) of findings has gained substantial attention within the social and behavioral sciences as well as artificial intelligence (AI). While these concerns have evolved over the past decade and have seen recognition in top-tier journals, they have recently extended their reach into the field of AI. Inconsistencies in terminologies have led to the adoption of precise definitions from Goodman et al. (2016) [7]. Reproducibility refers to consistent computational results under the same conditions, replicability involves achieving consistent results on similar datasets, and generalizability pertains to consistent results across different experimental contexts. AI's reproducibility studies have mostly targeted empirical and computational AI, focusing on open datasets, code availability, and metadata documentation. However, efforts towards the replicability of AI research have remained...

                                              Figure 1: Detecting tables and extracting table cell structures in document image (Fig 2 in  Fischer et al. ) In the past decades, several works have been published on detecting and extracting tables both from in-text-tables and  tables appearing in born-digital or scanned PDF documents  [ Pyreddi et al. ]. Early work focus on using heuristics such as character alignment in table images to extract tables [ Pyreddi et al. ]. Recent works involve detecting the corners of the table cells and inferring their connectivity [ Seo et al. ] in document images (see Figure 2).                                                           Figure 2: Detecting table cell corners (Fig 2 in...

                                                                                Figure 1: Traditional  and Deep Learning Approaches to Table Recognition ( Hashmi et al. ) Table recognition refers to the process of using optical character recognition (OCR) and machine learning (ML) models to identify the rows, columns, and individual text cells in tables in digital documents either born-digital or scan PDFs. The task of table recognition has been under investigation for more than two decades  for automatically extracting textual information from a variety of tables  [ Kieninger et al. , Wei et al. ].  Automatic table recognition can be very challenging due to tables having different structures, data types, and misaligned data e...